Investigating acceptable level of travel demand before capacity enhancement for signalized urban road networks

Increasing travel demand in urban areas triggers traffic congestion and increases delay in road networks. In this context, local authorities that are responsible for traffic operations seek to strike a balance between traffic volume and capacity to reduce total travel time on road networks. Since signalized intersections are the most critical components of road networks in terms of safety and operational issues, adjusting intersection signal timings becomes an effective method for authorities. When this tool remains incapable of overcoming traffic congestions, authorities take expensive measures such as increasing link capacities, lane additions or applying grade-separated junctions. However, it may be more useful to handle road networks as a whole by investigating the effects of optimizing signal timings of all intersections in the network. Therefore, it would be useful to investigate the right time for capacity enhancement on urban road networks to avoid premature investments considering limited resources of local authorities. In this study, effects of increasing travel demand on Total Travel Cost (TTC) is investigated by developing a bi-level programming model, called TRAvel COst Minimizer (TRACOM), in which the upper level minimizes the TTC subject to the stochastic user equilibrium link flows determined at the lower level. The TRACOM is applied to Allsop and Charlesworths’ network for different common origin-destination demand multipliers. Results revealed that TTC values showed an approximate linear increase while the travel demand is increased up to 16%. After this value, TTC showed a sudden spike although the travel demand was linearly increased that means optimizing signal timings must be supported by applying capacity enhancement countermeasures

Reserve Capacity Model for Optimizing Traffic Signal Timings with an Equity Constraint

This paper represents a solution algorithm for optimizing traffic signal timings in urban road networks by considering reserve capacity with an equity constraint. It is well known that the variation of signal timings in a road network may cause an inequity issue with regard to the travel costs of road users travelling between origin-destination (O-D) pairs. That is, the users may be influenced differently by changing traffic signal timings. In this context, the bilevel programming model is proposed for finding reserve capacity for signalized road networks by taking into account the equity issue. In the upper level, the reserve capacity is maximized with an equity constraint, whereas deterministic user equilibrium problem is dealt in the lower level. In order to solve the proposed model, a heuristic solution algorithm based on harmony search combined with a penalty function approach is developed. The application of the proposed model is illustrated for an example road network taken from a literature

Benchmark for Tuning Metaheuristic Optimization Technique to Optimize Traffic Light Signals Timing

Traffic congestion at intersections is an international problem in the cities. This problem causes more waiting time, air pollution, petrol consumption, stress of people and healthy problems. Against this background, this research presents a benchmark iterative approach for optimal use of the metaheuristic optimization techniques to optimize the traffic light signals timing problem. A good control of the traffic light signals timing on road networks may help in solving the traffic congestion problems. The aim of this research is to identify the most suitable metaheuristic optimization technique to optimize the traffic light signals timing problem, thus reducing average travel time (ATT) for each vehicle, waiting time, petrol consumption by vehicles and air pollution to the lowest possible level/degree. The central part of Nablus road network has a huge traffic congestion at the traffic light signals. It was selected as a research case study and was represented by the SUMO simulator. The researcher used a random algorithm and three different metaheuristic optimization techniques: three types of Genetic Algorithm (GA), Particle Swarm Algorithm (PS) and five types of Tabu Search Algorithm (TS). Parameters in each metaheuristic algorithm affect the efficiency of the algorithm in finding the optimal solutions. The best values of these parameters are difficult to be determined; their values were assumed in the previous traffic light signals timing optimization research. The efficiency of the metaheuristic algorithm cannot be ascertained of being good or bad. Therefore, the values of these parameters need a tuning process but this cannot be done by using SUMO simulator because of its heavy computation. The researcher used a benchmark iterative approach to tune the values of them etaheuristic algorithm parameters by using a benchmark function. The chosen function has similar characteristics to the traffic light signals timing problem. Then, through the use of this approach, the researcher arrived at the optimal use of the metaheuristic optimization algorithms to optimize traffic light signals timing problem. The efficiency of each metaheuristic optimization algorithm, tested in this research, is in finding the optimal or near optimal solution after using the benchmark iterative approach. The results of metaheuristic optimization algorithm improved at some values of the tuned parameters. The researcher validated the research results by comparing average results of the metaheuristic algorithms, used in solving the traffic light signals optimization problem after using benchmark iterative approach, with the average results of the same metaheuristic algorithms used before using the benchmark iterative approach; they were also compared with the results of Webster, HCM methods and SYNCHRO simulator. In the light of these study findings, the researcher recommends trying the benchmark iterative approach to get ore efficient solutions which are very close to the optimal solution for the traffic light signals timing optimization problem and many complex practical optimization problems that we face in real life.الازدحامات المرورية عند التقاطعات هي مشكله عالمية في المدن. هذه المشكلة تسبب المزيد من وقت االنتظار وتلوث الهواء و استهالك الوقود، و توتر الناس و مشاكل صحية. على هذه الخلفية، يقدم هذا البحث نهج المعيار المكرر لالستخدام تقنيات التحسين التخمينية في تحسين مشكلة توقيت اإلشارات الضوئية. التحكم الجيد في توقيت االشارات الضوئية على شبكات الطرق قد يساعد في حل مشاكل االزدحام المروري. يهدف هذا البحث الى تحديد أفضل و أنسب تقنية تحسين تخمينية لتحسين مشكلة توقيت االشارات الضوئية، وبالتالي تقليل متوسط الوقت الذي يستغرقه السفر (ATT(لكل مركبة، و وقت االنتظار، و استهالك الوقود المستخدم في المركبات و تلوث الهواء إلى أدنى مستوى ممكن. يعاني الجزء المركزي من شبكة طرق مدينة نابلس من ازدحام مروري كبير على االشارات الضوئية. و تم اختيار هذا الجزء كحالة البحث الدراسية و التي تم تمثيلها باستخدام برنامج المحاكاة سومو. و استخدم الباحث خوارزمية عشوائية و ثالث تقنيات تحسين تخمينية و هي: ثالث انواع من الخوارزمية الجينية، و خورزمية سرب الجسيمات، و خمسة انواع من خوارزمية التابو. و هناك متغيرات في كل خوارزمية تخمينية تؤثر على فعالية الخوارمية في ايجاد الحلول المثلى. و من الصعب تحديد افضل القيم لهذه المتغيرات؛ و قيم هذه المتغيرات كانت تفترض في ابحات تحسين توقيت االشارات الضوئية السابقة. وفي هذه الحاله فعالية اقتران التحسين التخميني ال يمكن التحقق منها اذا ما كانت جيده او سيئة. ولذلك فان قيم هذه المتغيرات بحاجه لعملية ضبط ، ولكن ال يمكننا ذلك باستخدام برنامج المحاكاه سومو النه حساباته ثقيله و طويله. استخدم الباحث طريقة مقارنة الدوال لضبط قيم متغيرات خوارزمية التحسين التخمينية باستخدام خوارزمية معيار. خوارمية المعيار المختاره لها خصائص شبيهه بمشكلة توقيت االشارات الضوئية. ثم من خالل استخدام هذه الطريقة، وصل الباحث الى افضل استخدام لخوارزميات التحسين التخمينية لتحسين مشكلة توقيت االشارات االضوئية. وفي هذا البحث تم اختبار فعالية كل خوارمية تحسين تخمينية في ايجاد الحل االمثل او حل قريب من الحل االمثل بعد ضبط خوارزمية التحسين التخمينية. لقد تحسنت نتائج خوارزمية التحسين التخمينية عند بعض قيم المتغيرات التي تم ضبطها. قام الباحث بالتحقق من نتائج البحث بمقارنة معدل نتائج خوارزميات التحسين التخمينية التي امستخدمها في تحسين مشكلة توقيت االشارات الضوئية قبل ضبط خوارزمية التحسين التخمينية، مع معدل نتائج نفس الخوارزميات التخمينية التي امستخدمها بعد ضبط خوارزمية التحسين التخمينية؛ وهذه النتائج تمت مقارنتها مع نتائج طريقتي ويبستر و HCM و برنامج السنكرو. في ضوء نتائج هذه الدراسة، يوصي الباحث بتجريب طريقة مقارنة الدوال لضبط خوارزميات التحسين التخمينية للحصول على حلول فعالة اكثر و التي تكون قريبة جدا من الحل االمثل لتحسين مشكلة توقيت االشارات الضوئية و لتحسين المشاكل العملية المعقدة التي تواجهنا في الحياة العملية

Mixed Integer Programming Approaches to Novel Vehicle Routing Problems

This thesis explores two main topics. The first is how to incorporate data on meteorological forecasts, traffic patterns, and road network topology to utilize deicing resources more efficiently. Many municipalities throughout the United States find themselves unable to treat their road networks fully during winter snow events. Further, as the global climate continues to change, it is expected that both the number and severity of extreme winter weather events will increase for large portions of the US.We propose to use network flows, resource allocation, and vehicle routing mixed integer programming approaches to be able to incorporate all of these data in a winter road maintenance framework. We also show that solution approaches which have proved useful in network flows and vehicle routing problems can be adapted to construct high-quality solutions to this new problem quickly. These approaches are validated on both random and real-world instances using data from Knoxville, TN.In addition to showing that these approaches can be used to allocate resources effectively given a certain deicing budget, we also show that these same approaches can be used to help determine a resource budget given some allocation utility score. As before, we validate these approaches using random and real-world instances in Knoxville, TN.The second topic considered is formulating mixed integer programming models which can be used to route automated electric shuttles. We show that these models can also be used to determine fleet composition and optimal vehicle characteristics to accommodate various demand scenarios. We adapt popular vehicle routing solution techniques to these models, showing that these strategies continue to be relevant and robust. Lastly, we validate these techniques by looking at a case study in Greenville, SC, which recently received a grant from the Federal Highway Administration to deploy a fleet of automated electric shuttles in three neighborhoods